Journal ArticleDOI
The FERET evaluation methodology for face-recognition algorithms
TLDR
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems.Abstract:
Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1,199 individuals are included in the FERET database, which is divided into development and sequestered portions of the database. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to 1) assess the state of the art, 2) identify future areas of research, and 3) measure algorithm performance.read more
Citations
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Proceedings ArticleDOI
Discriminative multi-manifold analysis for face recognition from a single training sample per person
Jiwen Lu,Yap-Peng Tan,Gang Wang +2 more
TL;DR: A novel discriminative multi-manifold analysis (DMMA) method by learning discriminating features from image patches is proposed to address the problem of not enough samples for discriminant learning in appearance-based face recognition methods.
Book ChapterDOI
Tensor sparse coding for region covariances
TL;DR: This paper proposes a novel approach for sparse representation of positive definite matrices, where vectorization would have destroyed the inherent structure of the data.
Journal ArticleDOI
VIPLFaceNet: an open source deep face recognition SDK
TL;DR: An open source face recognition method with deep representation named as VIPLFaceNet, which is a 10-layer deep convolutional neural network with seven Convolutional layers and three fully-connected layers, which achieves 98.60% mean accuracy on LFW using one single network.
Proceedings ArticleDOI
Attribute preserved face de-identification
TL;DR: This paper recognizes the need of de-identifying a face image while preserving a large set of facial attributes, which has not been explicitly studied before and forms an objective function and uses gradient descent to learn the optimal weights for fusing k images.
Journal ArticleDOI
Robust gender recognition by exploiting facial attributes dependencies
Juan Bekios-Calfa,Juan Bekios-Calfa,José Miguel Buenaposada,José Miguel Buenaposada,Luis Baumela,Luis Baumela +5 more
TL;DR: The existence of dependencies among gender, age and pose facial attributes is confirmed and it is proved that the performance and robustness of gender classifiers can be improved by exploiting these dependencies.
References
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